Overview

Dataset statistics

Number of variables20
Number of observations1103
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.8 KiB
Average record size in memory139.1 B

Variable types

Numeric13
Categorical4
Boolean3

Alerts

realSum is highly overall correlated with bedroomsHigh correlation
cleanliness_rating is highly overall correlated with guest_satisfaction_overallHigh correlation
guest_satisfaction_overall is highly overall correlated with cleanliness_ratingHigh correlation
bedrooms is highly overall correlated with realSumHigh correlation
dist is highly overall correlated with attr_index and 3 other fieldsHigh correlation
attr_index is highly overall correlated with dist and 3 other fieldsHigh correlation
attr_index_norm is highly overall correlated with dist and 3 other fieldsHigh correlation
rest_index is highly overall correlated with dist and 3 other fieldsHigh correlation
rest_index_norm is highly overall correlated with dist and 3 other fieldsHigh correlation
room_type is highly overall correlated with room_shared and 1 other fieldsHigh correlation
room_shared is highly overall correlated with room_typeHigh correlation
room_private is highly overall correlated with room_typeHigh correlation
room_shared is highly imbalanced (95.1%)Imbalance
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
dist has unique valuesUnique
metro_dist has unique valuesUnique
attr_index has unique valuesUnique
attr_index_norm has unique valuesUnique
rest_index has unique valuesUnique
rest_index_norm has unique valuesUnique
bedrooms has 83 (7.5%) zerosZeros

Reproduction

Analysis started2023-08-11 15:53:47.688010
Analysis finished2023-08-11 15:54:25.538422
Duration37.85 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551
Minimum0
Maximum1102
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:25.817640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.1
Q1275.5
median551
Q3826.5
95-th percentile1046.9
Maximum1102
Range1102
Interquartile range (IQR)551

Descriptive statistics

Standard deviation318.55298
Coefficient of variation (CV)0.57813608
Kurtosis-1.2
Mean551
Median Absolute Deviation (MAD)276
Skewness0
Sum607753
Variance101476
MonotonicityStrictly increasing
2023-08-11T17:54:26.091360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
733 1
 
0.1%
739 1
 
0.1%
738 1
 
0.1%
737 1
 
0.1%
736 1
 
0.1%
735 1
 
0.1%
734 1
 
0.1%
732 1
 
0.1%
758 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1102 1
0.1%
1101 1
0.1%
1100 1
0.1%
1099 1
0.1%
1098 1
0.1%
1097 1
0.1%
1096 1
0.1%
1095 1
0.1%
1094 1
0.1%
1093 1
0.1%

realSum
Real number (ℝ)

Distinct534
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean545.02053
Minimum128.88712
Maximum7782.9072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:26.539170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.88712
5-th percentile210.57812
Q1309.79776
median430.24863
Q3657.3243
95-th percentile1178.4266
Maximum7782.9072
Range7654.0201
Interquartile range (IQR)347.52654

Descriptive statistics

Standard deviation416.97431
Coefficient of variation (CV)0.76506167
Kurtosis88.040199
Mean545.02053
Median Absolute Deviation (MAD)150.21208
Skewness6.432379
Sum601157.64
Variance173867.58
MonotonicityNot monotonic
2023-08-11T17:54:26.840508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614.4400441 15
 
1.4%
307.220022 11
 
1.0%
393.4572212 11
 
1.0%
196.6114405 11
 
1.0%
209.0314719 10
 
0.9%
552.8085675 10
 
0.9%
467.0400487 10
 
0.9%
319.6400534 10
 
0.9%
737.2343168 9
 
0.8%
504.0658027 9
 
0.8%
Other values (524) 997
90.4%
ValueCountFrequency (%)
128.8871183 1
0.1%
143.6505519 1
0.1%
144.8222529 2
0.2%
161.9290887 1
0.1%
165.6785321 1
0.1%
167.3189136 1
0.1%
175.7551613 1
0.1%
177.1612026 2
0.2%
178.0985635 1
0.1%
180.2076254 1
0.1%
ValueCountFrequency (%)
7782.907225 1
0.1%
3637.663159 1
0.1%
3004.944579 1
0.1%
2771.307384 1
0.1%
2556.183067 1
0.1%
2500.878776 1
0.1%
2486.115342 1
0.1%
2458.228857 1
0.1%
2061.959553 1
0.1%
2058.210109 1
0.1%

room_type
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
Private room
559 
Entire home/apt
538 
Shared room
 
6

Length

Max length15
Median length12
Mean length13.457842
Min length11

Characters and Unicode

Total characters14844
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowPrivate room
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Private room 559
50.7%
Entire home/apt 538
48.8%
Shared room 6
 
0.5%

Length

2023-08-11T17:54:27.139375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-11T17:54:27.376512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
room 565
25.6%
private 559
25.3%
entire 538
24.4%
home/apt 538
24.4%
shared 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 1668
11.2%
r 1668
11.2%
e 1641
11.1%
t 1635
11.0%
m 1103
7.4%
a 1103
7.4%
1103
7.4%
i 1097
7.4%
P 559
 
3.8%
v 559
 
3.8%
Other values (7) 2708
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12100
81.5%
Space Separator 1103
 
7.4%
Uppercase Letter 1103
 
7.4%
Other Punctuation 538
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1668
13.8%
r 1668
13.8%
e 1641
13.6%
t 1635
13.5%
m 1103
9.1%
a 1103
9.1%
i 1097
9.1%
v 559
 
4.6%
h 544
 
4.5%
n 538
 
4.4%
Other values (2) 544
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
P 559
50.7%
E 538
48.8%
S 6
 
0.5%
Space Separator
ValueCountFrequency (%)
1103
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13203
88.9%
Common 1641
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1668
12.6%
r 1668
12.6%
e 1641
12.4%
t 1635
12.4%
m 1103
8.4%
a 1103
8.4%
i 1097
8.3%
P 559
 
4.2%
v 559
 
4.2%
h 544
 
4.1%
Other values (5) 1626
12.3%
Common
ValueCountFrequency (%)
1103
67.2%
/ 538
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1668
11.2%
r 1668
11.2%
e 1641
11.1%
t 1635
11.0%
m 1103
7.4%
a 1103
7.4%
1103
7.4%
i 1097
7.4%
P 559
 
3.8%
v 559
 
3.8%
Other values (7) 2708
18.2%

room_shared
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1097 
True
 
6
ValueCountFrequency (%)
False 1097
99.5%
True 6
 
0.5%
2023-08-11T17:54:27.543059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
True
559 
False
544 
ValueCountFrequency (%)
True 559
50.7%
False 544
49.3%
2023-08-11T17:54:27.719321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

person_capacity
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
2.0
656 
4.0
333 
3.0
79 
6.0
 
24
5.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3309
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 656
59.5%
4.0 333
30.2%
3.0 79
 
7.2%
6.0 24
 
2.2%
5.0 11
 
1.0%

Length

2023-08-11T17:54:27.905426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-11T17:54:28.100788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 656
59.5%
4.0 333
30.2%
3.0 79
 
7.2%
6.0 24
 
2.2%
5.0 11
 
1.0%

Most occurring characters

ValueCountFrequency (%)
. 1103
33.3%
0 1103
33.3%
2 656
19.8%
4 333
 
10.1%
3 79
 
2.4%
6 24
 
0.7%
5 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2206
66.7%
Other Punctuation 1103
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1103
50.0%
2 656
29.7%
4 333
 
15.1%
3 79
 
3.6%
6 24
 
1.1%
5 11
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 1103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1103
33.3%
0 1103
33.3%
2 656
19.8%
4 333
 
10.1%
3 79
 
2.4%
6 24
 
0.7%
5 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1103
33.3%
0 1103
33.3%
2 656
19.8%
4 333
 
10.1%
3 79
 
2.4%
6 24
 
0.7%
5 11
 
0.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
780 
True
323 
ValueCountFrequency (%)
False 780
70.7%
True 323
29.3%
2023-08-11T17:54:28.310334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

multi
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
0
763 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1103
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

Length

2023-08-11T17:54:28.491757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-11T17:54:28.673385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

Most occurring characters

ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1103
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 763
69.2%
1 340
30.8%

biz
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
0
976 
1
127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1103
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

Length

2023-08-11T17:54:28.859430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-11T17:54:29.042697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1103
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 976
88.5%
1 127
 
11.5%

cleanliness_rating
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4614687
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:29.197969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79820148
Coefficient of variation (CV)0.08436338
Kurtosis6.7442662
Mean9.4614687
Median Absolute Deviation (MAD)0
Skewness-2.0618151
Sum10436
Variance0.63712561
MonotonicityNot monotonic
2023-08-11T17:54:29.438370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 659
59.7%
9 334
30.3%
8 87
 
7.9%
7 11
 
1.0%
6 9
 
0.8%
4 2
 
0.2%
5 1
 
0.1%
ValueCountFrequency (%)
4 2
 
0.2%
5 1
 
0.1%
6 9
 
0.8%
7 11
 
1.0%
8 87
 
7.9%
9 334
30.3%
10 659
59.7%
ValueCountFrequency (%)
10 659
59.7%
9 334
30.3%
8 87
 
7.9%
7 11
 
1.0%
6 9
 
0.8%
5 1
 
0.1%
4 2
 
0.2%
Distinct32
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.362647
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:29.666956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile84
Q192
median96
Q398
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0896912
Coefficient of variation (CV)0.064534976
Kurtosis26.18274
Mean94.362647
Median Absolute Deviation (MAD)3
Skewness-3.383583
Sum104082
Variance37.084339
MonotonicityNot monotonic
2023-08-11T17:54:29.947278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
100 190
17.2%
98 122
11.1%
96 121
11.0%
97 95
8.6%
95 82
7.4%
94 69
 
6.3%
93 67
 
6.1%
99 65
 
5.9%
90 51
 
4.6%
92 48
 
4.4%
Other values (22) 193
17.5%
ValueCountFrequency (%)
20 1
 
0.1%
47 1
 
0.1%
60 3
0.3%
65 1
 
0.1%
70 1
 
0.1%
73 1
 
0.1%
74 1
 
0.1%
75 2
0.2%
76 1
 
0.1%
77 1
 
0.1%
ValueCountFrequency (%)
100 190
17.2%
99 65
 
5.9%
98 122
11.1%
97 95
8.6%
96 121
11.0%
95 82
7.4%
94 69
 
6.3%
93 67
 
6.1%
92 48
 
4.4%
91 30
 
2.7%

bedrooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2828649
Minimum0
Maximum5
Zeros83
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:30.171132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74017837
Coefficient of variation (CV)0.57697296
Kurtosis2.1404905
Mean1.2828649
Median Absolute Deviation (MAD)0
Skewness1.1359222
Sum1415
Variance0.54786402
MonotonicityNot monotonic
2023-08-11T17:54:30.381745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 719
65.2%
2 218
 
19.8%
0 83
 
7.5%
3 74
 
6.7%
4 7
 
0.6%
5 2
 
0.2%
ValueCountFrequency (%)
0 83
 
7.5%
1 719
65.2%
2 218
 
19.8%
3 74
 
6.7%
4 7
 
0.6%
5 2
 
0.2%
ValueCountFrequency (%)
5 2
 
0.2%
4 7
 
0.6%
3 74
 
6.7%
2 218
 
19.8%
1 719
65.2%
0 83
 
7.5%

dist
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8416213
Minimum0.015058798
Maximum11.1871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:30.638264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.015058798
5-th percentile0.42700018
Q11.3020583
median2.3413656
Q33.6481385
95-th percentile7.443446
Maximum11.1871
Range11.172041
Interquartile range (IQR)2.3460802

Descriptive statistics

Standard deviation2.1232449
Coefficient of variation (CV)0.74719487
Kurtosis2.2768627
Mean2.8416213
Median Absolute Deviation (MAD)1.1738733
Skewness1.4569638
Sum3134.3083
Variance4.5081688
MonotonicityNot monotonic
2023-08-11T17:54:30.941988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.022963798 1
 
0.1%
1.908567063 1
 
0.1%
4.486620645 1
 
0.1%
8.436536017 1
 
0.1%
1.729196947 1
 
0.1%
2.091818702 1
 
0.1%
0.4099399981 1
 
0.1%
6.842851866 1
 
0.1%
1.31950798 1
 
0.1%
2.325287056 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
0.01505879807 1
0.1%
0.09965350127 1
0.1%
0.1148056768 1
0.1%
0.1228203248 1
0.1%
0.1386645584 1
0.1%
0.1613319628 1
0.1%
0.1673131478 1
0.1%
0.1757999097 1
0.1%
0.1847578359 1
0.1%
0.1885288279 1
0.1%
ValueCountFrequency (%)
11.18710015 1
0.1%
11.03804499 1
0.1%
11.03773604 1
0.1%
10.96723627 1
0.1%
10.89864664 1
0.1%
10.85349274 1
0.1%
10.72982971 1
0.1%
10.72348325 1
0.1%
10.48852457 1
0.1%
10.45321955 1
0.1%

metro_dist
Real number (ℝ)

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0894387
Minimum0.036529935
Maximum4.4119153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:31.208440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.036529935
5-th percentile0.19378681
Q10.46298277
median0.85600979
Q31.5106295
95-th percentile2.9073901
Maximum4.4119153
Range4.3753854
Interquartile range (IQR)1.0476467

Descriptive statistics

Standard deviation0.83654649
Coefficient of variation (CV)0.76786929
Kurtosis1.8106196
Mean1.0894387
Median Absolute Deviation (MAD)0.47180533
Skewness1.3692336
Sum1201.6508
Variance0.69981002
MonotonicityNot monotonic
2023-08-11T17:54:31.476651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.539380003 1
 
0.1%
0.6992974213 1
 
0.1%
0.7057572605 1
 
0.1%
4.030013942 1
 
0.1%
0.4963551695 1
 
0.1%
1.517031912 1
 
0.1%
0.2774761465 1
 
0.1%
2.249047727 1
 
0.1%
1.233194724 1
 
0.1%
1.499233818 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
0.03652993497 1
0.1%
0.0383546937 1
0.1%
0.04522023671 1
0.1%
0.0484628245 1
0.1%
0.05105210307 1
0.1%
0.05493078887 1
0.1%
0.05679341113 1
0.1%
0.05689155184 1
0.1%
0.05769814365 1
0.1%
0.06871843184 1
0.1%
ValueCountFrequency (%)
4.411915315 1
0.1%
4.143226904 1
0.1%
4.135221094 1
0.1%
4.095711512 1
0.1%
4.030013942 1
0.1%
3.95047039 1
0.1%
3.946521658 1
0.1%
3.929398124 1
0.1%
3.911722316 1
0.1%
3.882222537 1
0.1%

attr_index
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.0099
Minimum40.931415
Maximum1888.5504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:31.745912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.931415
5-th percentile61.533722
Q1127.90987
median208.18031
Q3386.44224
95-th percentile599.62869
Maximum1888.5504
Range1847.619
Interquartile range (IQR)258.53238

Descriptive statistics

Standard deviation197.04689
Coefficient of variation (CV)0.72708374
Kurtosis9.6690992
Mean271.0099
Median Absolute Deviation (MAD)106.48261
Skewness2.057118
Sum298923.92
Variance38827.477
MonotonicityNot monotonic
2023-08-11T17:54:32.020996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.69037927 1
 
0.1%
312.7830516 1
 
0.1%
106.6760639 1
 
0.1%
54.92704857 1
 
0.1%
389.7122247 1
 
0.1%
322.3916213 1
 
0.1%
532.1488235 1
 
0.1%
69.38420307 1
 
0.1%
344.1277961 1
 
0.1%
181.5168762 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
40.93141537 1
0.1%
41.27584675 1
0.1%
41.34581474 1
0.1%
41.64218464 1
0.1%
42.01023157 1
0.1%
42.02512119 1
0.1%
42.64524544 1
0.1%
42.9510542 1
0.1%
43.90667928 1
0.1%
44.05838787 1
0.1%
ValueCountFrequency (%)
1888.550428 1
0.1%
1861.001507 1
0.1%
1502.553669 1
0.1%
1238.466777 1
0.1%
1171.698732 1
0.1%
1030.28114 1
0.1%
1029.727093 1
0.1%
1003.308116 1
0.1%
996.1632313 1
0.1%
843.9920247 1
0.1%

attr_index_norm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.350154
Minimum2.1673456
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:32.304707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1673456
5-th percentile3.2582514
Q16.7729124
median11.023286
Q320.462373
95-th percentile31.750738
Maximum100
Range97.832654
Interquartile range (IQR)13.689461

Descriptive statistics

Standard deviation10.433764
Coefficient of variation (CV)0.72708374
Kurtosis9.6690992
Mean14.350154
Median Absolute Deviation (MAD)5.6383252
Skewness2.057118
Sum15828.22
Variance108.86343
MonotonicityNot monotonic
2023-08-11T17:54:32.582390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.166707868 1
 
0.1%
16.56207041 1
 
0.1%
5.648568465 1
 
0.1%
2.908423718 1
 
0.1%
20.63552124 1
 
0.1%
17.07085056 1
 
0.1%
28.17763379 1
 
0.1%
3.673939655 1
 
0.1%
18.22179546 1
 
0.1%
9.611439203 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
2.167345641 1
0.1%
2.185583511 1
0.1%
2.189288363 1
0.1%
2.204981346 1
0.1%
2.224469675 1
0.1%
2.225258091 1
0.1%
2.258094082 1
0.1%
2.274286859 1
0.1%
2.324887842 1
0.1%
2.332920912 1
0.1%
ValueCountFrequency (%)
100 1
0.1%
98.54126635 1
0.1%
79.56121514 1
0.1%
65.57763876 1
0.1%
62.0422264 1
0.1%
54.55407092 1
0.1%
54.5247338 1
0.1%
53.12583138 1
0.1%
52.747505 1
0.1%
44.6899385 1
0.1%

rest_index
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean341.54119
Minimum50.877318
Maximum1435.1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:32.850495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.877318
5-th percentile76.7461
Q1163.46925
median260.25703
Q3469.29062
95-th percentile823.21913
Maximum1435.1024
Range1384.2251
Interquartile range (IQR)305.82138

Descriptive statistics

Standard deviation236.61108
Coefficient of variation (CV)0.69277465
Kurtosis0.58047537
Mean341.54119
Median Absolute Deviation (MAD)131.65548
Skewness1.0590337
Sum376719.93
Variance55984.802
MonotonicityNot monotonic
2023-08-11T17:54:33.269996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.25389587 1
 
0.1%
424.8028021 1
 
0.1%
132.1598902 1
 
0.1%
67.75326378 1
 
0.1%
432.2252507 1
 
0.1%
342.1987911 1
 
0.1%
718.0178209 1
 
0.1%
85.84094839 1
 
0.1%
450.7620228 1
 
0.1%
225.6820037 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
50.87731805 1
0.1%
51.29423178 1
0.1%
51.38730756 1
0.1%
51.79848967 1
0.1%
52.24171152 1
0.1%
52.24369565 1
0.1%
53.03613817 1
0.1%
53.51539664 1
0.1%
54.66234758 1
0.1%
54.83772631 1
0.1%
ValueCountFrequency (%)
1435.102401 1
0.1%
1402.15391 1
0.1%
1147.147422 1
0.1%
1144.936404 1
0.1%
1077.008769 1
0.1%
1044.506889 1
0.1%
1023.904785 1
0.1%
1022.004125 1
0.1%
994.770135 1
0.1%
969.6423035 1
0.1%

rest_index_norm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.799081
Minimum3.5452047
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:33.556382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5452047
5-th percentile5.3477787
Q111.390772
median18.135084
Q332.700846
95-th percentile57.363093
Maximum100
Range96.454795
Interquartile range (IQR)21.310074

Descriptive statistics

Standard deviation16.4874
Coefficient of variation (CV)0.69277465
Kurtosis0.58047537
Mean23.799081
Median Absolute Deviation (MAD)9.1739434
Skewness1.0590337
Sum26250.387
Variance271.83437
MonotonicityNot monotonic
2023-08-11T17:54:33.831554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.846472824 1
 
0.1%
29.60087042 1
 
0.1%
9.209091291 1
 
0.1%
4.721144898 1
 
0.1%
30.11807732 1
 
0.1%
23.84490409 1
 
0.1%
50.03251479 1
 
0.1%
5.981520785 1
 
0.1%
31.40974627 1
 
0.1%
15.72584671 1
 
0.1%
Other values (1093) 1093
99.1%
ValueCountFrequency (%)
3.54520472 1
0.1%
3.574255869 1
0.1%
3.580741523 1
0.1%
3.609393284 1
0.1%
3.64027762 1
0.1%
3.640415877 1
0.1%
3.695634412 1
0.1%
3.729029831 1
0.1%
3.808951023 1
0.1%
3.821171665 1
0.1%
ValueCountFrequency (%)
100 1
0.1%
97.70410177 1
0.1%
79.93488282 1
0.1%
79.78081586 1
0.1%
75.04752053 1
0.1%
72.7827428 1
0.1%
71.34715853 1
0.1%
71.21471779 1
0.1%
69.3170142 1
0.1%
67.56607076 1
0.1%

lng
Real number (ℝ)

Distinct1022
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8911576
Minimum4.7755
Maximum5.01077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:34.099311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7755
5-th percentile4.829621
Q14.871
median4.89001
Q34.907315
95-th percentile4.968632
Maximum5.01077
Range0.23527
Interquartile range (IQR)0.036315

Descriptive statistics

Standard deviation0.038882079
Coefficient of variation (CV)0.0079494636
Kurtosis1.180797
Mean4.8911576
Median Absolute Deviation (MAD)0.01801
Skewness0.32041189
Sum5394.9468
Variance0.0015118161
MonotonicityNot monotonic
2023-08-11T17:54:34.424675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.91 6
 
0.5%
4.895 4
 
0.4%
4.88971 3
 
0.3%
4.896 3
 
0.3%
4.946 3
 
0.3%
4.874 3
 
0.3%
4.893 3
 
0.3%
4.90369 3
 
0.3%
4.878 3
 
0.3%
4.875 3
 
0.3%
Other values (1012) 1069
96.9%
ValueCountFrequency (%)
4.7755 1
0.1%
4.77975 1
0.1%
4.78027 1
0.1%
4.78534 1
0.1%
4.78555 1
0.1%
4.78591 1
0.1%
4.78696 1
0.1%
4.78922 1
0.1%
4.78942 1
0.1%
4.78951 1
0.1%
ValueCountFrequency (%)
5.01077 1
0.1%
5.00974 1
0.1%
5.00769 1
0.1%
5.00683 1
0.1%
5.00633 1
0.1%
5.0049 1
0.1%
5.00369 1
0.1%
5.00232 1
0.1%
5.0021 1
0.1%
5.00072 1
0.1%

lat
Real number (ℝ)

Distinct950
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.364858
Minimum52.2911
Maximum52.42348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2023-08-11T17:54:34.691714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52.2911
5-th percentile52.332222
Q152.35458
median52.36559
Q352.37526
95-th percentile52.395936
Maximum52.42348
Range0.13238
Interquartile range (IQR)0.02068

Descriptive statistics

Standard deviation0.019467444
Coefficient of variation (CV)0.00037176542
Kurtosis2.1350288
Mean52.364858
Median Absolute Deviation (MAD)0.01046
Skewness-0.47546214
Sum57758.438
Variance0.00037898136
MonotonicityNot monotonic
2023-08-11T17:54:35.019743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.362 5
 
0.5%
52.365 4
 
0.4%
52.373 4
 
0.4%
52.37062 4
 
0.4%
52.394 4
 
0.4%
52.36 4
 
0.4%
52.366 3
 
0.3%
52.364 3
 
0.3%
52.37075 3
 
0.3%
52.36376 3
 
0.3%
Other values (940) 1066
96.6%
ValueCountFrequency (%)
52.2911 1
0.1%
52.2915 1
0.1%
52.29304 1
0.1%
52.29306 1
0.1%
52.2963 1
0.1%
52.2972 1
0.1%
52.2977 1
0.1%
52.29838 1
0.1%
52.29921 1
0.1%
52.29924 1
0.1%
ValueCountFrequency (%)
52.42348 1
0.1%
52.42343 1
0.1%
52.42337 1
0.1%
52.42258 1
0.1%
52.42006 1
0.1%
52.419 1
0.1%
52.4185 1
0.1%
52.41772 1
0.1%
52.41746 1
0.1%
52.41659 1
0.1%

Interactions

2023-08-11T17:54:22.246338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:50.359198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:53.130381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:56.065772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:58.747192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:01.624637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:04.248730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:06.892620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:09.571003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:12.289892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.801401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:17.272097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:19.890501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:22.443145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:50.561991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:53.342296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:56.277975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:58.949414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:01.839670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:04.423789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:07.086761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:09.777770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:12.480476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.979983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:17.449046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.063322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:22.635641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:50.893419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:53.546508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:56.506872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:59.168527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:02.065041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:04.624152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:07.271560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:10.038239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:12.672971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:15.177394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:17.678896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.252838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:22.825426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:51.113984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:53.782187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:56.704674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:59.374663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:02.243172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:04.846201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:07.469035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:10.253340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:12.884126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:15.371591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:17.881954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.433206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:23.041247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:51.329406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:54.049194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:56.941180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:59.770138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:02.470840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:05.075639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:07.681307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:10.472328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:13.109349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:15.587225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:18.258784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.630941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:23.282261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:51.541581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:54.266200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:57.131935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:59.963348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:02.679154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:05.266566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:07.864471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:10.674775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:13.286416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:15.765018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:18.445171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.803315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:23.458720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:51.738797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:54.492995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:57.334492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:00.163999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:02.870815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:05.466880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:08.027102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:10.886904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:13.471225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:15.947403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:18.611740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:20.990245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:23.640453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:51.920793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:54.687222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:57.506548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:00.367209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:03.040827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:05.644323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:08.191771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:11.082860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:13.649001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:16.135427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:18.779383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:21.154424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:23.826298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:52.104434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:54.925267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:57.699036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:00.559420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:03.243062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:05.848274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:08.422595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:11.278060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:13.859982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:16.331433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:18.966100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:21.345970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:24.002805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:52.336538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:55.142649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:57.903282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:00.741747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:03.449909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:06.047940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:08.592928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:11.484535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.037059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:16.512590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:19.150567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:21.527515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:24.205659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:52.556963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:55.389346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:58.127579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:00.959269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:03.654618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:06.254846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:08.972555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:11.699634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.222815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:16.696087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:19.339058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:21.715076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:24.403666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:52.736379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:55.638496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:58.307960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:01.169612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:03.845291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:06.475736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:09.165083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:11.896714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.400034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:16.879676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:19.532043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:21.904521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:24.578854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:52.928757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:55.843538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:53:58.513038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:01.382746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:04.030705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:06.673798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:09.369769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:12.080810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:14.588286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:17.088935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:19.713855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-11T17:54:22.068663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-11T17:54:35.234417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0realSumcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglatroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibiz
Unnamed: 01.000-0.034-0.0070.015-0.0100.0280.030-0.021-0.021-0.027-0.027-0.036-0.0400.0050.0000.0420.0000.0270.0540.165
realSum-0.0341.000-0.0030.1750.523-0.390-0.1930.4100.4100.4000.400-0.0450.0840.2160.0000.3090.2440.0000.0590.000
cleanliness_rating-0.007-0.0031.0000.571-0.022-0.0060.0010.0010.0010.0030.003-0.0390.0030.0160.0000.0320.0240.3430.0000.067
guest_satisfaction_overall0.0150.1750.5711.0000.1310.0110.060-0.016-0.016-0.015-0.015-0.0630.0260.0480.0000.0940.0000.3270.0000.130
bedrooms-0.0100.523-0.0220.1311.0000.0560.014-0.040-0.040-0.049-0.049-0.025-0.0330.3110.0000.4370.3910.0990.1190.125
dist0.028-0.390-0.0060.0110.0561.0000.401-0.951-0.951-0.961-0.9610.037-0.4070.1870.1160.2360.0000.0970.0700.139
metro_dist0.030-0.1930.0010.0600.0140.4011.000-0.488-0.488-0.483-0.483-0.1610.2480.0810.0000.1150.0000.0000.0000.154
attr_index-0.0210.4100.001-0.016-0.040-0.951-0.4881.0001.0000.9890.989-0.0880.2060.0000.0000.0560.0000.0000.0000.138
attr_index_norm-0.0210.4100.001-0.016-0.040-0.951-0.4881.0001.0000.9890.989-0.0880.2060.0000.0000.0560.0000.0000.0000.138
rest_index-0.0270.4000.003-0.015-0.049-0.961-0.4830.9890.9891.0001.000-0.0820.2190.1160.0000.1750.0000.0000.0850.165
rest_index_norm-0.0270.4000.003-0.015-0.049-0.961-0.4830.9890.9891.0001.000-0.0820.2190.1160.0000.1750.0000.0000.0850.165
lng-0.036-0.045-0.039-0.063-0.0250.037-0.161-0.088-0.088-0.082-0.0821.000-0.0870.1230.0670.1570.0450.0240.1080.127
lat-0.0400.0840.0030.026-0.033-0.4070.2480.2060.2060.2190.219-0.0871.0000.1120.0730.1400.0000.0000.0000.075
room_type0.0050.2160.0160.0480.3110.1870.0810.0000.0000.1160.1160.1230.1121.0001.0001.0000.2680.1680.2400.074
room_shared0.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0670.0731.0001.0000.0550.0470.0000.0000.000
room_private0.0420.3090.0320.0940.4370.2360.1150.0560.0560.1750.1750.1570.1401.0000.0551.0000.3740.1680.2350.074
person_capacity0.0000.2440.0240.0000.3910.0000.0000.0000.0000.0000.0000.0450.0000.2680.0470.3741.0000.0820.1540.092
host_is_superhost0.0270.0000.3430.3270.0990.0970.0000.0000.0000.0000.0000.0240.0000.1680.0000.1680.0821.0000.0900.132
multi0.0540.0590.0000.0000.1190.0700.0000.0000.0000.0850.0850.1080.0000.2400.0000.2350.1540.0901.0000.236
biz0.1650.0000.0670.1300.1250.1390.1540.1380.1380.1650.1650.1270.0750.0740.0000.0740.0920.1320.2361.000

Missing values

2023-08-11T17:54:24.882370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-11T17:54:25.353857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0realSumroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibizcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglat
00194.033698Private roomFalseTrue2.0False1010.093.015.0229642.53938078.6903794.16670898.2538966.8464734.9056952.41772
11344.245776Private roomFalseTrue4.0False008.085.010.4883890.239404631.17637833.421209837.28075758.3429284.9000552.37432
22264.101422Private roomFalseTrue2.0False019.087.015.7483123.65162175.2758773.98590895.3869556.6467004.9751252.36103
33433.529398Private roomFalseTrue4.0False019.090.020.3848620.439876493.27253426.119108875.03309860.9735654.8941752.37663
44485.552926Private roomFalseTrue2.0True0010.098.010.5447380.318693552.83032429.272733815.30574056.8116774.9005152.37508
55552.808567Private roomFalseTrue3.0False008.0100.022.1314201.904668174.7889579.255191225.20166215.6923764.8769952.38966
66215.124317Private roomFalseTrue2.0False0010.094.011.8810920.729747200.16765210.599010242.76552416.9162514.9157052.38296
772771.307384Entire home/aptFalseFalse4.0True0010.0100.031.6868071.458404208.80810911.056528272.31382318.9752194.8846752.38749
881001.804420Entire home/aptFalseFalse4.0False009.096.023.7191411.196112106.2264565.624761133.8762029.3286864.8645952.40175
99276.521454Private roomFalseTrue2.0False1010.088.013.1423610.924404206.25286210.921226238.29125816.6044784.8760052.34700
Unnamed: 0realSumroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibizcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglat
10931093909.474375Entire home/aptFalseFalse4.0False008.086.022.1359190.256965405.48655321.470782460.67673832.1006184.8870152.35440
10941094228.716050Private roomFalseTrue3.0True0010.098.017.6287662.92777361.7921693.27193676.4666015.3283034.7916252.34422
10951095378.693788Entire home/aptFalseFalse2.0False009.083.013.3267251.330570181.2981489.599857216.22858515.0671194.8616252.35037
10961096295.034331Private roomFalseTrue2.0False018.086.014.8804300.884337107.3347375.683446132.6991379.2466674.8442252.34116
10971097356.197127Private roomFalseTrue4.0True0010.095.012.1692530.212113224.43355611.883906291.78572620.3320494.9111852.35718
109810982486.115342Entire home/aptFalseFalse2.0False0010.0100.012.3758331.436054181.1983559.594573225.30420315.6995214.8586952.37677
10991099233.637194Private roomFalseTrue2.0False1010.090.014.7197360.322263108.0248805.719989134.7752779.3913354.8361152.34910
11001100317.062311Private roomFalseTrue2.0False1010.092.010.5983550.751993440.47651523.323524625.94756243.6169274.8889752.37798
110111011812.855904Entire home/aptFalseFalse4.0False008.084.051.9430200.388532257.94810513.658524336.58923723.4540224.9068852.35794
11021102258.008577Shared roomTrueFalse2.0False008.090.010.2802040.516217533.96265328.273677807.49230756.2672264.8929552.37575